System and method for aggregating test plot results based on agronomic environments

ABSTRACT

A system to receive data representing agronomic responses based on randomized replicated treatments conducted in test plots of agronomic environments, aggregate the data representing the agronomic responses into subsets of the data representing the agronomic responses, each subset of the data representing the agronomic responses associated with one of a number of performance zones, receive characteristics associated with a portion of a field and determine that the portion of the field represents a particular performance zone of the number of performance zones based on the characteristics associated with the portion of the field, recommend a particularized treatment level for a crop located in the portion of the field based on the particular performance zone, and communicate the particularized treatment level to a machine, the particularized treatment level to be applied to the portion of the field by the machine to optimize an agronomic response based on the particular performance zone.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/444,874 and entitled “System and Method for Aggregating Test PlotResults Based on Agronomic Environments”, which claims the benefit ofU.S. Patent Application No. 62/757,267, filed Nov. 8, 2018, entitled“Aggregate Enhanced Learning Block Responses,” the entire contents ofwhich is incorporated herein by reference.

BACKGROUND

As the demand on the food supply increases and the total viable farmlanddecreases, methods and systems are needed that maximize crop yields.Maximum crop yields result in increased production of agriculturalproducts and more value per acre of land. However, the effort inmaximizing crop yields is difficult, time consuming, and costly in partbecause the characteristics of farmland vary from acre to acre, fromfield to field, and even within fields. This variance is due to factorssuch as the conditions of the soil and topography, among others.Further, an agricultural farm field may include significant acre-to-acrevariations in nutrients, quality of crop produced, and ultimately, cropyield.

The conventional practice is to prescribe agricultural inputs, such asseed and fertilizer, to the entire agricultural farm field according tothe needs of the most deficient soil, or according to the averagedrequirements of the different soils. The result is a substantial areathat can receive either more or less of the item being applied than whatthe site specific areas can efficiently use to produce agronomic output.This can result in either a significant waste of agricultural inputs orunrealized yield potential.

Growers and their agronomic advisors can make more accurate inputdecisions with access to more accurate data of site specific agronomicresponses. Agronomic decision making has been driven by a research modelthat involves yield and other observations from small plots with varioustreatments. Examples would be yield by applied nitrogen rates or seedingrate. Such testing suffers from the limitation of being able totranslate the results observed in a small plot at a research farm toproduction fields, which typically have different conditions of soils,fertility, and management practices, among others.

It is with these issues in mind, among others, that various aspects ofthe disclosure were conceived.

SUMMARY

According to one aspect, a system and method is provided for aggregatingenhanced learning blocks by performance zone. As an example, the systemmay include at least one server computing device that collects dataassociated with a plurality of enhanced test plots. The data may beobtained from a database and may be agronomic responses based onrandomized replicated treatments conducted in test plots of agronomicenvironments, with such test plots having such randomized replicatedtreatments in particular agronomic environments also being referred toas enhanced test plots. An enhanced test plot may be arandomized/replicated single treatment factor experiment that has beenplaced in the target agronomic environment/performance zone of aproduction field.

An exemplary commercial embodiment of such enhanced test plots includeEnhanced Learning Block® testing by Premier Crop Systems LLC. Similaragronomic environments may be classified based on factors orcharacteristics as a particular performance zone among a number ofdifferent performance zones, e.g., performance zone one. A particularperformance zone may yield an average agronomic response across allobserved treatment replicates based on a particular treatment level. Thesystem may determine a particular performance zone for a portion of afield and may provide a recommendation of a particularized treatmentlevel based on the data associated with the plurality of enhanced testplots. The data associated with the enhanced test plots may be collectedover a time period, e.g., a number of years for a particular geographiclocation. Associated weather data may be collected to characterize theimpact of weather on the agronomic response to the respective treatmenttype (e.g., seeding rate vs. nitrogen rate). Other managementinformation is also captured as part of the process for eachexperimental area. The other management information may include,previous crop type, soil tillage/preparation practices, seed geneticsplanted, seeding rate, date planted, date harvested, details about cropprotection products used, and plant nutrients applied, among others.This other management information may be useful to further refine theagronomic response in a particular performance zone. As an example, itmay be used to determine that a similar response in performance zone onefor genetics segment A may be expected.

As a result, the system can provide the recommendation and therecommendation can be sent to a machine to apply the particularizedtreatment level to the agronomic environment/performance zone in a fieldto optimize an agronomic response. The recommendation may be interactiveand personalized based on user input. The user can utilize weatherforecast information to select the likely weather scenario as well asprovide information for the cost of the input and the anticipatedselling price for a unit of output allowing the recommendation to betailored to the likely weather and economic conditions that a growerwill face.

A system may include a memory and at least one processor to receive,from a database, data representing agronomic responses based onrandomized replicated treatments conducted in test plots of agronomicenvironments, aggregate the data representing the agronomic responsesinto subsets of the data representing the agronomic responses, eachsubset of the data representing the agronomic responses associated withone of a number of performance zones, receive characteristics associatedwith a portion of a field and determine that the portion of the fieldrepresents a particular performance zone of the number of performancezones based on the characteristics associated with the portion of thefield, recommend a particularized treatment level for a crop located inthe portion of the field based on the particular performance zone, andcommunicate the particularized treatment level to a machine, theparticularized treatment level to be applied to the portion of the fieldby the machine to optimize an agronomic response based on the particularperformance zone.

According to another aspect, a method includes receiving, from adatabase, by at least one processor, data representing agronomicresponses based on randomized replicated treatments conducted in testplots of agronomic environments, aggregating, by the at least oneprocessor, the data representing the agronomic responses into subsets ofthe data representing the agronomic responses, each subset of the datarepresenting the agronomic responses associated with one of a number ofperformance zones, receiving, by the at least one processor,characteristics associated with a portion of a field and determiningthat the portion of the field represents a particular performance zoneof the number of performance zones based on the characteristicsassociated with the portion of the field, recommending, by the at leastone processor, a particularized treatment level for a crop located inthe portion of the field based on the particular performance zone, andcommunicating, by the at least one processor, the particularizedtreatment level to a machine, the particularized treatment level to beapplied to the portion of the field by the machine to optimize anagronomic response based on the particular performance zone.

According to an additional aspect, a non-transitory computer-readablestorage medium includes instructions stored thereon that, when executedby a computing device cause the computing device to perform operations,the operations including receiving, from a database, data representingagronomic responses based on randomized replicated treatments conductedin test plots of agronomic environments, aggregating the datarepresenting the agronomic responses into subsets of the datarepresenting the agronomic responses, each subset of the datarepresenting the agronomic responses associated with one of a number ofperformance zones, receiving characteristics associated with a portionof a field and determining that the portion of the field represents aparticular performance zone of the number of performance zones based onthe characteristics associated with the portion of the field,recommending a particularized treatment level for a crop located in theportion of the field based on the particular performance zone, andcommunicating the particularized treatment level to a machine, theparticularized treatment level to be applied to the portion of the fieldby the machine to optimize an agronomic response based on the particularperformance zone.

These and other aspects, features, and benefits of the presentdisclosure will become apparent from the following detailed writtendescription of the preferred embodiments and aspects taken inconjunction with the following drawings, although variations andmodifications thereto may be effected without departing from the spiritand scope of the novel concepts of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate embodiments and/or aspects of thedisclosure and, together with the written description, serve to explainthe principles of the disclosure. Wherever possible, the same referencenumbers are used throughout the drawings to refer to the same or likeelements of an embodiment, and wherein:

FIG. 1 is a block diagram of a system for aggregating enhanced test plotresponses by performance zone according to an example embodiment.

FIG. 2 illustrates an image of enhanced test plots according to anexample embodiment.

FIG. 3 illustrates a plurality of performance zones and representativecurves associated with agronomic responses according to an exampleembodiment.

FIG. 4 illustrates a map showing a plurality of performance zones in ageographic region according to an example embodiment.

FIG. 5 illustrates a block diagram of a server computing device of thesystem according to an example embodiment.

FIG. 6 illustrates a graph showing curves indicating average yield basedon treatment rate generated by the system according to an exampleembodiment.

FIG. 7 illustrates a flowchart of a process for aggregating enhancedtest plots and determining an optimal treatment for a performance zonebased on the aggregated enhanced test plots according to an exampleembodiment.

FIG. 8 illustrates a block diagram of an example computer device for usewith the example embodiments.

DETAILED DESCRIPTION

For the purpose of promoting an understanding of the principles of thepresent disclosure, reference will now be made to the embodimentsillustrated in the drawings and specific language will be used todescribe the same. It will, nevertheless, be understood that nolimitation of the scope of the disclosure is thereby intended; anyalterations and further modifications of the described or illustratedembodiments, and any further applications of the principles of thedisclosure as illustrated therein are contemplated as would normallyoccur to one skilled in the art to which the disclosure relates.

The client computing devices and the server computing devices discussedherein may communicate over a communications network using HypertextTransfer Protocol (HTTP) and/or other communications protocols. HTTPprovides a request-response protocol in the client-server computingmodel. A client application running on the client computing device maybe a client and a server application running on the server computingdevice may be the server, e.g., a web server. The client submits, forexample, an HTTP request to the server. The web server of the servercomputing device provides resources, such as Hypertext Markup Language(HTML) files and/or other content, and performs other functions onbehalf of the client, and returns an HTTP response message to theclient. Other types of communications using different protocols may beused in other examples.

The one or more computing devices may communicate based onrepresentational state transfer (REST) and/or Simple Object AccessProtocol (SOAP). As an example, a first computer (e.g., a clientcomputer) may send a request message that is a REST and/or a SOAPrequest formatted using Javascript Object Notation (JSON) and/orExtensible Markup Language (XML). In response to the request message, asecond computer (e.g., a server computer) may transmit a REST and/orSOAP response formatted using JSON and/or XML.

An enhanced test plot treatment is a scientific experiment placed in asingle agronomic environment, and contains treatment levels that arerandomized and replicated within an experimental area (e.g., seedingrate or plant density). The agronomic response, namely yield, may beprovided in a series of graph curves as a function of treatment (e.g.,treatment rate observed) for each agronomic environment. As an example,the treatment may be nitrogen and may include a fertilizer whichdelivers nitrogen.

The agronomic responses (e.g., yields) may be aggregated responses froma plurality of fields in a geographical area having a similar agronomicenvironment, also known as a performance zone. A field may have oneperformance zone type. Alternatively, a subset or portion of a field mayhave a performance zone type. A field also may have multiple differenttypes of performance zones assigned to each portion of the field. As anexample, a first portion of a field may be performance zone one and asecond portion of a field may be performance zone two. The agronomicresponses from a plurality of fields having the same agronomicenvironment may be aggregated together to provide a prediction,forecast, or estimation of future agronomic responses within a same orsimilar agronomic environment or performance zone. An operator canpredict an agronomic response in a performance zone by varying atreatment level applied. The operator can predict or choose a yield of acrop by measuring or controlling the treatment level applied. Theoperator also can vary relevant management practice segments and provideinformation associated with a weather forecast. Accordingly, byaggregating the responses generated by enhanced test plot treatmentsinto subsets of responses for each similar agronomic environment orperformance zone, yield can be predicted and/or controlled. The crop maybe one or more of corn, legumes, soybeans, or another type of crop.

Aspects of a system and method may include at least one server computingdevice that collects data associated with a plurality of test plots. Thedata may be agronomic responses based on randomized replicatedtreatments conducted in test plots of agronomic environments, such testplots having such randomized replicated treatments in particularagronomic environments also being referred to as enhanced test plots. Anexemplary commercial embodiment of such enhanced test plots includesEnhanced Learning Block® testing. Similar agronomic environments may beclassified based on factors or characteristics as a particularperformance zone among a plurality of different performance zones, e.g.,performance zone one. A particular performance zone may yield anagronomic response based on a particular treatment level, weatherconditions for the period of crop growth and harvest, as well asrelevant management practice segments (e.g., seed genetics of segmentA). The system may determine a particular performance zone for a portionof a field and may provide a recommendation of a particularizedtreatment level based on the data associated with the plurality ofenhanced test plots. In one example, the data may be associated withtens, hundreds or thousands of enhanced test plots. The data associatedwith the enhanced test plots may be collected over a time period, e.g.,a number of years for a particular geographic location. As a result, thesystem can provide the recommendation and the recommendation can be sentto a machine to apply the particularized treatment level to the field tooptimize an agronomic response.

Alternatively, information associated with the particularized treatmentlevel may be sent to a user and/or an agricultural advisor that mayprovide this information to an operator of an agricultural location suchas a farm.

The particularized treatment and/or particularized treatment level mayinclude nitrogen or other fertilizers (e.g., phosphorus, potassium,sulfur, boron, zinc), seeding rate/plant population, hybrid/variety, orother crop amendments or treatments that may be applied in a variablemanner with equipment (e.g., tillage depth, residue management, use offungicide).

As an example, based on enhanced test plot data, it may be known that ifan average treatment amount is 150 units, this may yield an average of215 bushels of crop per acre (statistical confidence of +/−20 bushelsper acre) in performance zone one and management practice segment A. Inaddition, if the average treatment amount is 170 units, this may yieldan average of 224 bushels of crop per acre (statistical confidence of+/−15 bushels per acre). If the average treatment amount is 190, thismay yield an average of 206 bushels per acre (statistical confidence of+/−20 bushels per acre). Based on this enhanced test plot data, it isdesirable to use the average treatment amount of 170 units to providethe optimal agronomic response and financial gain when also consideringthe weather forecast for the remainder of the growing season as well aseconomic conditions (cost of the input, anticipated selling price of theoutput).

As another example, based on enhanced test plot data, it may be knownthat if an average treatment amount is 30000 seeds/acre ($112.50/acinvestment), this may yield an average of 240 bushels of crop per acre(statistical confidence of +/−19 bushels per acre) for a gross return of$840, and a net return of $727.50/ac after accounting for the seedinput. If the average treatment amount is 32000 seeds ($120/acinvestment), this may yield an average of 246 bushels per acre(statistical confidence of +/−23 bushels per acre) for a gross return of$861, and a net return of $741/ac after accounting for the seed input.If the average treatment amount is 34000 seeds per acre ($127.50/acinvestment), this may yield an average of 259 bushels per acre(statistical confidence of +/−26 bushels per acre) for a gross return of$906.50, and a net return of $779/ac after accounting for the seedinput. Based on this enhanced test plot data, it is desirable to use theaverage treatment amount of 34000 units to provide the optimizedagronomic response and financial gain. More sophisticated approaches canbe taken to account for weather conditions/forecast as well as growertolerance of risk to provide an optimized recommendation or treatmentlevel for the particular performance zone/management practice.

In one example, the system may include a memory and at least oneprocessor to receive, from a database, data representing agronomicresponses based on randomized replicated treatments conducted in testplots of agronomic environments, aggregate the data representing theagronomic responses into subsets of the data representing the agronomicresponses, each subset of the data representing the agronomic responsesassociated with one of a number of performance zones, receivecharacteristics associated with a portion of a field and determine thatthe portion of the field represents a particular performance zone of thenumber of performance zones based on the characteristics associated withthe portion of the field, recommend a particularized treatment level fora crop located in the portion of the field based on the particularperformance zone and relevant management practice segments, andcommunicate the particularized treatment level to a machine, theparticularized treatment level to be applied to the portion of the fieldby the machine to optimize an agronomic response based on the particularperformance zone.

FIG. 1 shows a block diagram of a computing system comprising a systemfor aggregating enhanced test plot responses by performance zone 100according to an example embodiment. The system 100 includes at least oneclient computing device 102 and at least one machine 104 incommunication with at least one server computing device 106 via acommunications network 108. The machine 104 may be an agriculturalmachine for applying agricultural inputs. As an example, the machine 104may include a dispensing system for dispensing agricultural input andmay be one of a tractor, a planter, an air seeder, a sprayer(ground-based or air-based), irrigation equipment, tillage equipment,and a harvester, among others.

The at least one client computing device 102, the machine 104, and theat least one server computing device 106 may together provide andexecute an optimal treatment application 110. The client computingdevice 102 may execute a first client component of the optimal treatmentapplication 110, the machine 104 may execute a second component of theoptimal treatment application 110, and the server computing device 106may execute a third component of the optimal treatment application 110.The server computing device 106 may be in communication with arelational database management system (RDBMS) or another type ofdatabase management system that stores and communicates data from atleast one database 112.

The at least one database 112 may be a structured query language (SQL)database such as a MySQL database, a NoSQL database, or a MongoDBdatabase, among others. The at least one database 112 may be integratedwith the server computing device 106 or in communication with the servercomputing device 106.

The at least one client computing device 102 is configured to receivedata from and/or transmit data to the machine 104 and the at least oneserver computing device 106 through the communications network 108.Although the at least one server computing device 106 is shown as asingle server computing device, it is contemplated that the at least oneserver computing device 106 may include multiple server computingdevices, for example, in a cloud computing configuration.

The communications network 108 can be the Internet, an intranet, oranother wired or wireless communications network. For example, thecommunications network 108 may include a Mobile Communications (GSM)network, a code division multiple access (CDMA) network, 3rd GenerationPartnership Project (GPP) network, an Internet Protocol (IP) network, awireless application protocol (WAP) network, a WiFi network, a Bluetoothnetwork, a satellite communications network, or an IEEE 802.11 standardsnetwork, as well as various communications thereof. Other conventionaland/or later developed wired and wireless networks may also be used.

The at least one client computing device 102 includes at least oneprocessor to process data and memory to store data. The processorprocesses communications, builds communications, retrieves data frommemory, and stores data to memory. The processor and the memory arehardware. The memory may include volatile and/or non-volatile memory,e.g., a computer-readable storage medium such as a cache, random accessmemory (RAM), read only memory (ROM), flash memory, or other memory tostore data and/or computer-readable executable instructions such as aportion or a component of the optimal treatment application 110. Inaddition, the at least one client computing device 102 further includesat least one communications interface to transmit and receivecommunications, messages, and/or signals.

The at least one client computing device 102 can be a laptop computer, asmartphone, a personal digital assistant, a handheld computer, a tabletcomputer, a standard personal computer, or another processing device.The at least one client computing device 102 may include a display, suchas a computer monitor, for displaying data and/or graphical userinterfaces. The at least one client computing device 102 may alsoinclude a Global Positioning System (GPS) hardware device fordetermining a particular location of the client computing device 102, aninput device, such as a camera, a keyboard or a pointing device (e.g., amouse, trackball, pen, or touch screen) to enter data into or interactwith graphical and/or other types of user interfaces. In an exemplaryembodiment, the display and the input device may be incorporatedtogether as a touch screen of the smartphone or tablet computer.

The at least one client computing device 102 may display on the displaya graphical user interface (or GUI) to generate a graphical userinterface on the display. The graphical user interface may be providedby the optimal treatment application 110. The graphical user interfaceenables a user of the at least one client computing device 102 tointeract with the optimal treatment application 110.

The optimal treatment application 110 may be a component of anapplication and/or service executable by the at least one clientcomputing device 102, the machine 104, and/or the at least one servercomputing device 106. For example, the optimal treatment application 110may be a single unit of deployable executable code or a plurality ofunits of deployable executable code. According to one aspect, theoptimal treatment application 110 may include one component that may bea web application, a native application, and/or a mobile application(e.g., an app) downloaded from a digital distribution applicationplatform that allows users to browse and download applications developedwith mobile software development kits (SDKs) including the App Store andGOOGLE PLAY®, among others.

The at least one machine 104 includes at least one processor to processdata and memory to store data. The processor processes communications,builds communications, retrieves data from memory, and stores data tomemory. The processor and the memory are hardware. The memory mayinclude volatile and/or non-volatile memory, e.g., a computer-readablestorage medium such as a cache, random access memory (RAM), read onlymemory (ROM), flash memory, or other memory to store data and/orcomputer-readable executable instructions such as a portion or acomponent of the optimal treatment application 110. In addition, the atleast one machine 104 further includes at least one communicationsinterface to transmit and receive communications, messages, and/orsignals. The at least one machine 104 may also include a GlobalPositioning System (GPS) hardware device for determining a particularlocation of the machine 104, an input device, such as a camera, akeyboard or a pointing device (e.g., a mouse, trackball, pen, or touchscreen) to enter data into or interact with graphical and/or other typesof user interfaces. In an exemplary embodiment, the display and theinput device may be incorporated together as a touch screen.

The at least one server computing device 106 includes at least oneprocessor to process data and memory to store data. The processorprocesses communications, builds communications, retrieves data frommemory, and stores data to memory. The processor and the memory arehardware. The memory may include volatile and/or non-volatile memory,e.g., a computer-readable storage medium such as a cache, random accessmemory (RAM), read only memory (ROM), flash memory, or other memory tostore data and/or computer-readable executable instructions such as aportion or a component of the optimal treatment application 110. Inaddition, the at least one server computing device 106 further includesat least one communications interface to transmit and receivecommunications, messages, and/or signals.

FIG. 2 illustrates an image 200 of an example enhanced test plotaccording to an example embodiment. As shown in FIG. 2, there are twodifferent enhanced test plots including block one 202 and block two 204.Block one 202 is associated with a first portion of an agriculturalfield and block two is associated with a second portion of theagricultural field. Further details associated with test plots andenhanced test plots are discussed in U.S. application Ser. No.14/833,670 entitled “System and Method for Controlling Machinery forRandomizing and Replicating Predetermined Input Levels,” issued as U.S.Pat. No. 10,123,474, the entire contents of which is incorporated hereinby reference.

In one example, the machine 104 may have the dispensing system andmodify dispensement of agricultural input from the dispensing system ineach location of the enhanced test plot of the field. A method forrandomizing and replicating predetermined agricultural inputs, includingtreatment or dispensement levels, within the enhanced test plot may beused in each portion of the field including block one 202 and block two204. As an example, at least two, or alternatively three or four,dispensements, such as application rates, for the agricultural input maybe defined. A number of replications for the at least two applicationrates for the agricultural input may be defined. The number ofreplications of each application rate may include two, three, four orany plurality. The application rates for the agricultural input and thenumber of replications for the application rates may be associated withthe portion of the field. At least one test plot or block contingentupon the number of application rates may be defined and the number ofreplications for the at least two application rates for the agriculturalinput may be defined. Locations (e.g., an area defined by machine orequipment constraints) for the treatments, such as dispensement levelsor application rates, within the enhanced test plot for the agriculturalinputs may be randomly assigned to the test plot or block. The size ofthe enhanced test plot, as well as the number of replications andtreatments (e.g., dispensements, rates of application) may be determinedbased on the constraints of the machine or equipment, and the number ofreplications and dispensements may be based on those required to producestatistically significant results. For instance, the size of theapplicator and/or harvester machine, the time a machine takes totransition between number and types of treatments (e.g., changingapplication rate or detecting a yield response), along with the numberof replications and type of treatments, may affect the amount of areaneeded for test plot to produce reliable and replicable data.

Yield data with an actual agricultural input level in the treatment areaof the test plot or block may be obtained to identify agronomicresponses to the treatment levels of the agricultural input. This datais stored in the database 112 and may be aggregated with data from othertest plots or blocks.

Each enhanced test plot may be used to perform an experiment and obtainexperimental results based on the agricultural input and associatedtreatment levels within a particular agronomic environment/performancezone. The agricultural input may be related to plant population,nitrogen, potassium, or another agricultural input. In addition, theexperiment may have an experimental crop such as corn, legume, soybeans,or another experimental crop. Each experiment may have particularcharacteristics or factors such as seed genetic type (managementpractice), agronomic environment, and management (management practices)characteristics. The characteristics or factors may be related to aparticular performance zone. Each performance zone may have particularagronomic environmental characteristics which indicate that performancezone. The crop management (management practices) characteristics may bea hybrid/variety (and associated seed company), relative maturity,traits related to chemical resistance, traits related to pesticideresistance, and traits related to disease resistance, among others.

The nutrient management (management practices) may be related to appliednutrients including an applied total Nitrogen, an applied totalPhosphorus, an applied total Potassium, and an applied total Sulfur,among others. The soil (agronomic) environment characteristics may berelated to a USDA soil mapping unit name, a USDA soil taxonomysuborder/order, a soil test pH, soil test cation exchange capacity(CEC), soil test organic matter, soil test Phosphorus, soil testPotassium, soil test Calcium, soil test Magnesium, soil test Potassiumbase saturation, biophysical productivity index, and a water holdingcapacity index, among others. The landscape characteristics may berelated to elevation, percentage slope, topographical wetness index, andslope category, among others. The productivity (agronomic environment)characteristics may be related to irrigation information, historicalaverage productivity rating, and historical maximum productivity rating,among others. The climate characteristics (longer term growing seasonweather trends) may be related to minimum/maximum/average daily airtemperature from April to May, precipitation from April to May,minimum/maximum/average daily air temperature from June to July,precipitation from June to July, minimum/maximum/average daily airtemperature from August to September, and precipitation from August toSeptember, among others. The weather characteristics (growing seasonweather conditions observed for the conducted experiments) may berelated to plant soil emergence period growing degree days (GDD),vegetative crop development period GDD, grain fill crop developmentperiod GDD, season total GDD, plant soil emergence period rainfall,vegetative crop development period rainfall, grain fill crop developmentperiod rainfall, and growing season total rainfall, among others.

In one example, the data in the database may be divided or aggregatedinto at least one subset of experimental results/data based onperformance zones and experiment type. A performance zone may representa portion or subset of an agricultural field and may have particularcharacteristics, key parameters, or key parameter values and result inthe same or similar agronomic response across the performance zone forthe experiment type. The particular agronomic environmentalcharacteristics may be used to identify the performance zone. Theparticular characteristics may include at least one of soil environment,landscape, productivity, climate, and weather. A further refinement ofcrop response for the experiment type in the aforementioned performancezone can be accomplished by considering the management practicesegments, for example, genetic response segmentation to the treatmentlevels in the particular performance zone. As an example, hybrids thatare grouped into “segment A” may respond to seeding rate treatments in asimilar manner in performance zone one. Nutrient management responsesegmentation also may be considered. As another example, there may be aresponse to nitrogen treatment levels in performance zone one when thereis low total applied nitrogen vs. high total applied nitrogen.

As an example, a first performance zone type may have a first set ofparticular agronomic environment characteristics, a second performancezone type may have a second set of agronomic environment particularcharacteristics, a third performance zone type may have a third set ofparticular agronomic environment characteristics, and a fourthperformance zone type may have a fourth set of particular agronomicenvironment characteristics. In some instances, the performance zonetype may be undetermined, which can aid the user in determining the needfor additional experiments to further refine theidentification/definition of performance zones.

The first set of agronomic environment characteristics may be in anorder or list that is weighted according to importance of defining aperformance zone. In other words, the agronomic environmentcharacteristics at the top of the order are more important and theseagronomic environment characteristics more likely lead to classificationin the first performance zone type. As an example, the order of thefirst set of characteristics may be a very low soil test Calcium level,a very low soil test Cation Exchange Capacity level, a strong slopeangle, a USDA soil mapping unit name of Hersey, a low soil testPotassium level, a USDA soil mapping unit name of Seaton, a low soiltest organic matter, a low soil test Magnesium level, a USDA soiltaxonomy suborder of Udalfs, and a landscape topographical wetness indexlevel of very high, among others.

A second set of agronomic environmental characteristics may be in anorder or list that is weighted according to importance of defining aperformance zone. In other words, the agronomic environmentcharacteristics at the top of the order are more important and theseagronomic environment characteristics more likely lead to classificationin the second performance zone type. As an example, the order of thesecond set of characteristics may be a soil test Cation ExchangeCapacity level that is moderate, a soil test Calcium level that ismoderate, a soil test organic matter level that is moderate, a landscapeslope angle of gentle slope, a USDA soil mapping unit name of Mt.Carroll, a USDA soil mapping unit name of Port Byron, a USDA soiltaxonomy suborder of Udalfs, a USDA soil mapping unit name of Timula, aproductivity level of high, and a USDA soil taxonomy suborder of Udepts,among others.

A third set of agronomic environmental characteristics may be in anorder or list that is weighted according to importance of defining aperformance zone. In other words, the characteristics at the top of theorder are more important and these characteristics more likely lead toclassification in the third performance zone type. As an example, theorder of the third set of characteristics may be soil test Potassiumlevel of very low, USDA soil taxonomy suborder of Ustolls, USDA soilmapping unit name of Port, and soil test pH level of very high, amongothers.

A fourth set of agronomic environmental characteristics may be in anorder or list that is weighted according to importance of defining aperformance zone. In other words, the characteristics at the top of theorder are more important and these characteristics more likely lead toclassification in the fourth performance zone type. As an example, theorder of the fourth set of characteristics may be soil test CationExchange Capacity level of high, soil test Calcium level of high, USDAsoil taxonomy suborder of Aquolls, soil test organic matter level ofhigh, soil test Magnesium level of high, USDA soil mapping unit name ofWebster, soil test Potassium Base Saturation level of very low, a USDAsoil mapping unit name of Klinger, a soil test Calcium level of veryhigh, and USDA a soil mapping unit name of Maxfield, among others.

FIG. 3 illustrates a representation 300 of a plurality of performancezones and representative curves associated with agronomic responsesaccording to an example embodiment. In one example, the representation300 may be an agricultural field having four different portions. Inanother example, the representation 300 may be four different fieldseach having a single portion. As shown in FIG. 3, a performance zone A302 is shown in the top left corner of the representation 300. Aperformance zone B 304 is shown in the top right corner of therepresentation 300. A performance zone C 306 is shown in the bottom leftcorner of the representation 300. A performance zone D 308 is shown inthe bottom right corner of the representation.

In addition, for each performance zone, there is a known response curvethat is based on treatment rate or level and expected yield that isgenerated based on multiple correspondence analysis. In one example, afirst axis (e.g., y axis) of the graph may be related to expected yield,based on experimental results. The second axis (e.g., x axis) of thegraph may be related to treatment rate or level. Based on a treatmentrate or level, there is an expected yield for each performance zonetype. The graph 310 is related to the performance zone A. The graph 312is related to the performance zone B. The graph 314 is related to theperformance zone C. The graph 316 is related to the performance zone D.

FIG. 4 illustrates a map 400 showing a plurality of performance zonesaccording to an example embodiment. As shown in FIG. 4, there are atotal of four different user interface elements that are used torepresent a performance zone on the map 400. A first user interfaceelement 402 is a rectangle and is used to represent a first performancezone type. A second user interface element 404 is a star and is used torepresent a second performance zone type. A third user interface element406 is a triangle and is used to represent a third performance zonetype. A fourth user interface element 408 is a cross and is used torepresent a fourth performance zone type. The performance zones arescattered throughout the map and some are on one side of a river 410 andothers are on the other side of the river. Some agricultural fields mayhave a plurality of portions and each portion may have a differentperformance zone. Although there are four different types of performancezones shown in FIG. 4, there may be more or less than four differenttypes of performance zones.

FIG. 5 illustrates a block diagram of the server computing device 106according to an example embodiment. The server computing device 106includes at least one processor 502 and computer readable media (CRM)204 in memory on which the optimal treatment application 110 or otheruser interface or application is stored. The computer readable media mayinclude volatile media, non-volatile media, removable media,non-removable media, and/or another available medium that can beaccessed by the processor. By way of example and not limitation, thecomputer readable media comprises computer storage media andcommunication media. Computer storage media includes non-transitorystorage memory, volatile media, non-volatile media, removable media,and/or non-removable media implemented in a method or technology forstorage of information, such as computer/machine-readable/executableinstructions, data structures, program modules, or other data.Communication media may embody computer/machine-readable/executableinstructions, data structures, program modules, or other data andinclude an information delivery media or system, both of which arehardware.

The optimal treatment application 110 may include an enhanced test plotdata collection module 506 for obtaining and collecting enhanced testplot data associated with a plurality of fields having a plurality ofportions of fields. The enhanced test plot data may be from a period oftime, e.g., a number of months or a number of years. The enhanced testplot (ETP) data collection module 506 may obtain the enhanced test plotdata from the database 112 or from another source. The enhanced testplot data may indicate agronomic responses based on randomizedreplicated treatments conducted in test plots of agronomic environments.In addition, the enhanced test plot data collection module 506 mayaggregate the data representing the agronomic responses into subsets ofthe data representing the agronomic responses, each subset of the datarepresenting the agronomic responses associated with one of a number ofperformance zones such as a performance zone type one or A, aperformance zone type two or B, a performance zone type three or C, anda performance zone type four or D, among others. In addition, theenhanced test plot data collection module 506 may aggregate the datarepresenting the agronomic responses into subsets of data such that eachsubset represents a particular period of time, e.g., one year. This mayprovide experimental results with similar growing season weather and/ormanagement practice segments that exhibit similar yield response withina particular performance zone.

As an example, a first subset of the data may represent agronomicresponses for performance zone one or A. A second subset of the data mayrepresent agronomic responses for performance zone two or B. A thirdsubset of the data may represent agronomic responses for performancezone three or C. A fourth subset of the data may represent agronomicresponses for performance zone four or D.

The optimal treatment application 110 may include a performance zonemodule 508 for receiving agronomic environmental characteristics orfactors associated with a portion of a field and determining that theportion of the field represents a particular performance zone of thenumber of performance zones based on the agronomic environmentalcharacteristics associated with the portion of the field. As an example,the performance zone module 508 may receive at least some of agronomicenvironmental characteristics discussed above such as at least one ofsoil environment, landscape, productivity, climate, and weather. Basedon the received characteristics, the performance zone module 508 maycompare the characteristics with the known characteristics forperformance zones such as the characteristics for performance zone one,the characteristics for performance zone two, the characteristics forperformance zone three, and the characteristics for performance zonefour. Based on the comparison, the performance zone module may determinethat the portion of the field is associated with a particularperformance zone.

The optimal treatment application 110 may include an optimal treatmentmodule 510 for recommending a particularized treatment level for a croplocated in the portion of the field based on the particular performancezone. The particularized treatment level for the crop may be based onthe information known about each performance zone such as the knowncurve that is based on treatment rate or level and expected yield aswell as other background management practices which may further refinethe observed response curve to the relevant treatment. These arediscussed herein as management practice segments. As discussed above,based on a treatment rate or level, there is an expected yield for eachperformance zone type. The graph 310 is related to the performance zoneA. The graph 312 is related to the performance zone B. The graph 314 isrelated to the performance zone C. The graph 316 is related to theperformance zone D.

The optimal treatment application 110 may include a machinecommunication module 512 for communicating the particularized treatmentlevel to the machine 104. The machine 104 may apply the particularizedtreatment level to the portion of the field to optimize an agronomicresponse based on the particular performance zone as well as forecastedweather for the remainder of the growing season, economic factors andgrower risk tolerance, among others. As an example, the optimizedagronomic response may be a maximized yield of the crop. Theparticularized treatment level may be communicated in a message ornotification to the machine 104 and may include GPS informationassociated with the portion of the field, machine input instructionssuch as tillage depth, planting depth, tillage angle, residue spreadwidth, a number of seeds per area, a weight of seeds per area, volumeper area, and weight per area, among others. The particularizedtreatment level may be communicated as a particular file or in aparticular format to the machine 104.

In addition, the optimal treatment application 110 includes a userinterface module 514 for displaying a user interface on the display ofthe client computing device 102, the machine 104, or another computingdevice. As an example, the user interface module 514 generates a nativeand/or web-based graphical user interface (GUI) that accepts input andprovides output viewed by users of the client computing device 102. Theclient computing device 102 may provide real-time automatically anddynamically refreshed optimal treatment information, among otherinformation. The user interface module 514 may send data to othermodules of the optimal treatment application 110, and retrieve data fromother modules of the optimal treatment application 110 asynchronouslywithout interfering with the display and behavior of the user interfacedisplayed by the client computing device 102.

As an example, the user interface module 514 may generate a userinterface that displays a graph of predicted yield of the crop based ona treatment rate for the portion of the field based on the particularperformance zone. As another example, the user interface module 514 maygenerate a user interface that displays a map showing the portion of thefield on the map as a user interface element that represents theparticular performance zone.

FIG. 6 illustrates a graph 600 showing response curves indicatingaverage yield based on treatment rate generated by the system accordingto an example embodiment. As shown in FIG. 6, a first axis of the graphis associated with average yield for a particular crop. A second axis ofthe graph is associated with treatment rate for the particular crop.Each point shown in the graph may be based on the enhanced test plotdata associated with an experimental replicate. The example isconstrained to yield response in a particular performance zone, howeverdifferent management factor segments exhibit different responses, suchas crop genetics segments A-D. As an example, the graph may show anumber of points and a curve that may be a standard area curve and aconfidence interval that may be generated and displayed that shows aresponse or yield that is to be expected based on a treatment rate orlevel. The confidence interval may be shown as an area that is above andbelow the curve and within a particular interval from the curve.

FIG. 6 shows a first curve 602 with a first confidence interval 604, asecond curve 606 with a second confidence interval 608, a third curve610 with a third confidence interval 612, and a fourth curve 614 with afourth confidence interval 616. Further refinements are possible withthe breakdown of response curve by management factor segment bydifferent growing season weather scenarios that have been observed aspart of the field experiments.

Based on these curves, an optimal treatment rate may be selected basedon a desired average yield to provide optimized agronomic response forthe crop. In one example, this may be where the yield provides a highestamount coupled with a lowest treatment rate or when the treatment levelhas a lowest price per unit or provides the greatest marginal return. Asan example, this graph 600 shows corn plant population experiments inperformance zone two. A previous crop in the particular portion of thefield was soybeans. Each curve may represent a particular segment orunique groupings of corn hybrids that respond similarly to plantpopulation changes in performance zone two. Using this information aboutperformance zone two, an optimized agronomic response may be providedfor the crop and an expected yield based on a particularized treatmentlevel may be determined based on the curve and the confidence intervalas well as the choice of management practice. As an example, theoptimized agronomic response may indicate an optimal corn hybrid toplant.

As a further example, the graph may be used to display response curvesbased on nutrient management factors such as corn plant populationexperiments in performance zone two where the previous crop in theparticular portion of the field was soybeans. Each curve may representyield response to the experimental treatment levels given differenttotal applied nitrogen level segments.

As a further example, the graph may be used to display response curvesbased on soil agronomic environment factors such as corn plantpopulation experiments in performance zone two where the previous cropin the particular portion of the field was soybeans. Each curve mayrepresent yield response to experimental treatment levels givendifferent soil test of Phosphorus ranges.

As a further example, the graph may be used to display response curvesbased on landscape factors such as corn plant population experiments inperformance zone two where the previous crop in the particular portionof the field was soybeans. Each curve may represent yield response toexperimental treatment levels given different elevation range levels.

As a further example, the graph may be used to display response curvesbased on productivity factors such as corn plant population experimentsin performance zone two where the previous crop in the particularportion of the field was soybeans. Each curve may represent yieldresponse to experimental treatment levels given different historicmaximum productivity rating levels.

As a further example, the graph may be used to display response curvesbased on weather factors such as corn plant population experiments inperformance zone two where the previous crop in the particular portionof the field was soybeans. Each curve may represent yield response toexperimental treatment levels given different ranges of average airtemperature from April to May.

As a further example, the graph may be used to display response curvesbased on weather factors such as corn plant population experiments inperformance zone two where the previous crop in the particular portionof the field was soybeans. Each curve may represent yield response toexperimental treatment levels given different vegetative cropdevelopment stage precipitation amount levels.

As a further example, the graph may be used to display response curvesbased on multiple factors or characteristics as a time such as cornplant population experiments in performance zone two where the previouscrop in the particular portion of the field was soybeans. As an examplethe first factor may be related to crop management (e.g., a group ofseed hybrids that respond in a similar manner to the treatment) and asecond factor may be related to soil test phosphorus levels. It may bedesirable to select a point where both curves meet and may provide anoptimal yield of the crop, but that may also require nutrient managementto change soil test phosphorus levels. Economic considerations can alsobe introduced as part of the analysis as well as forecasted growingseason weather to refine what is the optimal hybrid/variety selectionand soil test phosphorus level for a particular performance zone.

FIG. 7 illustrates a flowchart of a process 700 for aggregating enhancedlearning blocks and their associated data and determining an optimaltreatment based on the aggregated enhanced learning blocks according toan example embodiment.

In a first step 702, the optimal treatment application 110 of the servercomputing device 106 may receive data representing agronomic responsesbased on randomized replicated treatments conducted in test plots ofagronomic environments. As an example, the randomized replicatedtreatments are assigned to a field by multiplying a minimum treatmentarea by a predetermined number of treatment levels and a number of timesthe predetermined treatment levels is to be replicated and randomlyassigning a spatial location of each of the treatment levels to bereplicated in a test plot in the field, carrying out a treatment for thespatial location of each of the treatment levels in the test plot in thefield, obtaining data representing an agronomic response for the field,and storing the data representing the agronomic response for the fieldin the database 112. The randomized replicated treatments may beconducted a plurality of times in a plurality of fields in a geographicregion for a period of time, e.g., years.

In a next step 704, the optimal treatment application 110 of the servercomputing device 106 may aggregate the data representing the agronomicresponses into subsets of the data representing the agronomic response.Each subset of the data representing the agronomic responses may beassociated with one of a number of performance zones, e.g. performancezone one.

In step 706, the optimal treatment application 110 of the servercomputing device 106 may receive agronomic environmental characteristicsassociated with a portion of a field and determine that the portion ofthe field represents a particular performance zone of the number ofperformance zones based on the characteristics associated with theportion of the field.

In step 708, the optimal treatment application 110 of the servercomputing device 106 may recommend a particularized treatment level/ratefor a crop located in the portion of the field based on the particularperformance zone. In step 710, the optimal treatment application 110 ofthe server computing device 106 may communicate the particularizedtreatment level/rate to the machine 104. The particularized treatmentlevel/rate may be applied to the portion of the field by the machine 104to optimize an agronomic response based on the particular performancezone. As an example, the particularized treatment level/rate may beprovided by the treatment dispensing system of the machine 104. Thetreatment dispensing system may dispense the particularized treatmentlevel/rate on the particular portion of the field of at least one ofseeding, irrigation, nitrogen, fungicide, herbicide, insecticide,pesticide, and growth regulator.

The optimal treatment application 110 of the server computing device 106may generate a user interface that displays a graph of predicted yieldof the crop based on a treatment rate for the portion of the field basedon the particular performance zone. This is shown in FIG. 6. As anotherexample, the optimal treatment application 110 of the server computingdevice 106 may generate a user interface that displays a map showing theportion of the field on the map as a user interface element thatrepresents the particular performance zone. This is shown in FIG. 4.

FIG. 8 illustrates an example computing system 800 that may implementvarious systems, such as the client computing device 102, the machine104, the server computing 106, and the methods discussed herein, such asprocess 700. A general-purpose computer system 800 is capable ofexecuting a computer program product to execute a computer process. Dataand program files may be input to the computer system 800, which readsthe files and executes the programs therein such as the optimaltreatment application 110. Some of the elements of a general-purposecomputer system 800 are shown in FIG. 8 wherein a processor 802 is shownhaving an input/output (110) section 804, a central processing unit(CPU) 806, and a memory section 808. There may be one or more processors802, such that the processor 802 of the computer system 800 comprises asingle central-processing unit 806, or a plurality of processing units,commonly referred to as a parallel processing environment. The computersystem 800 may be a conventional computer, a server, a distributedcomputer, or any other type of computer, such as one or more externalcomputers made available via a cloud computing architecture. Thepresently described technology is optionally implemented in softwaredevices loaded in memory 808, stored on a configured DVD/CD-ROM 810 orstorage unit 812, and/or communicated via a wired or wireless networklink 814, thereby transforming the computer system 800 in FIG. 8 to aspecial purpose machine for implementing the described operations.

The memory section 808 may be volatile media, nonvolatile media,removable media, non-removable media, and/or other media or mediums thatcan be accessed by a general purpose or special purpose computingdevice. For example, the memory section 808 may include non-transitorycomputer storage media and communication media. Non-transitory computerstorage media further may include volatile, nonvolatile, removable,and/or non-removable media implemented in a method or technology for thestorage (and retrieval) of information, such ascomputer/machine-readable/executable instructions, data and datastructures, engines, program modules, and/or other data. Communicationmedia may, for example, embody computer/machine-readable/executable,data structures, program modules, algorithms, and/or other data. Thecommunication media may also include an information delivery technology.The communication media may include wired and/or wireless connectionsand technologies and be used to transmit and/or receive wired and/orwireless communications.

The I/O section 804 is connected to one or more user-interface devices(e.g., a keyboard 816 and a display unit 818), a disc storage unit 812,and a disc drive unit 820. Generally, the disc drive unit 820 is aDVD/CD-ROM drive unit capable of reading the DVD/CD-ROM medium 810,which typically contains programs and data 822. Computer programproducts containing mechanisms to effectuate the systems and methods inaccordance with the presently described technology may reside in thememory section 808, on a disc storage unit 812, on the DVD/CD-ROM medium810 of the computer system 800, or on external storage devices madeavailable via a cloud computing architecture with such computer programproducts, including one or more database management products, web serverproducts, application server products, and/or other additional softwarecomponents. Alternatively, a disc drive unit 820 may be replaced orsupplemented by a floppy drive unit, a tape drive unit, or other storagemedium drive unit. The network adapter 824 is capable of connecting thecomputer system 800 to a network via the network link 814, through whichthe computer system can receive instructions and data. Examples of suchsystems include personal computers, Intel or PowerPC-based computingsystems, AMD-based computing systems, ARM-based computing systems, andother systems running a WINDOWS-based, a UNIX-based, a LINUX-based, orother operating system. It should be understood that computing systemsmay also embody devices such as Personal Digital Assistants (PDAs),mobile phones, tablets or slates, multimedia consoles, gaming consoles,set top boxes, etc.

When used in a LAN-networking environment, the computer system 800 isconnected (by wired connection and/or wirelessly) to a local networkthrough the network interface or adapter 824, which is one type ofcommunications device. When used in a WAN-networking environment, thecomputer system 800 typically includes a modem, a network adapter, orany other type of communications device for establishing communicationsover the wide area network. In a networked environment, program modulesdepicted relative to the computer system 800 or portions thereof, may bestored in a remote memory storage device. It is appreciated that thenetwork connections shown are examples of communications devices for andother means of establishing a communications link between the computersmay be used.

In an example implementation, source code executed by the clientcomputing device 102, the machine 104, the server computing device 106,a plurality of internal and external databases, source databases, and/orcached data on servers are stored in the storage of, memory of theclient computing device 102, memory of the machine 104, memory of theserver computing device 106, or other storage systems, such as the diskstorage unit 812 or the DVD/CD-ROM medium 810, and/or other externalstorage devices made available and accessible via a networkarchitecture. The source code executed by the client computing device102, the machine 104, and the server computing device 106 may beembodied by instructions stored on such storage systems and executed bythe processor 802.

Some or all of the operations described herein may be performed by theprocessor 802, which is hardware. Further, local computing systems,remote data sources and/or services, and other associated logicrepresent firmware, hardware, and/or software configured to controloperations of the system 100 and/or other components. Such services maybe implemented using a general-purpose computer and specialized software(such as a server executing service software), a special purposecomputing system and specialized software (such as a mobile device ornetwork appliance executing service software), or other computingconfigurations. In addition, one or more functionalities disclosedherein may be generated by the processor 802 and a user may interactwith a Graphical User Interface (GUI) using one or more user-interfacedevices (e.g., the keyboard 816, the display unit 818, and the userdevices 804) with some of the data in use directly coming from onlinesources and data stores. The system set forth in FIG. 8 is but onepossible example of a computer system that may employ or be configuredin accordance with aspects of the present disclosure.

In the present disclosure, the methods disclosed may be implemented assets of instructions or software readable by a device. Further, it isunderstood that the specific order or hierarchy of steps in the methodsdisclosed are instances of example approaches. Based upon designpreferences, it is understood that the specific order or hierarchy ofsteps in the method can be rearranged while remaining within thedisclosed subject matter. The accompanying method claims presentelements of the various steps in a sample order, and are not necessarilymeant to be limited to the specific order or hierarchy presented.

The described disclosure may be provided as a computer program product,or software, that may include a non-transitory machine-readable mediumhaving stored thereon executable instructions, which may be used toprogram a computer system (or other electronic devices) to perform aprocess according to the present disclosure. A non-transitorymachine-readable medium includes any mechanism for storing informationin a form (e.g., software, processing application) readable by a machine(e.g., a computer). The non-transitory machine-readable medium mayinclude, but is not limited to, magnetic storage medium (e.g., floppydiskette), optical storage medium (e.g., CD-ROM); magneto-opticalstorage medium, read only memory (ROM); random access memory (RAM);erasable programmable memory (e.g., EPROM and EEPROM); flash memory; orother types of medium suitable for storing electronic executableinstructions.

The description above includes example systems, methods, techniques,instruction sequences, and/or computer program products that embodytechniques of the present disclosure. However, it is understood that thedescribed disclosure may be practiced without these specific details.

It is believed that the present disclosure and many of its attendantadvantages will be understood by the foregoing description, and it willbe apparent that various changes may be made in the form, constructionand arrangement of the components without departing from the disclosedsubject matter or without sacrificing all of its material advantages.The form described is merely explanatory, and it is the intention of thefollowing claims to encompass and include such changes.

While the present disclosure has been described with reference tovarious embodiments, it will be understood that these embodiments areillustrative and that the scope of the disclosure is not limited tothem. Many variations, modifications, additions, and improvements arepossible. More generally, embodiments in accordance with the presentdisclosure have been described in the context of particularimplementations. Functionality may be separated or combined in blocksdifferently in various embodiments of the disclosure or described withdifferent terminology. These and other variations, modifications,additions, and improvements may fall within the scope of the disclosureas defined in the claims that follow.

What is claimed is:
 1. A system comprising: a memory; and at least oneprocessor to: receive, from a database, data representing agronomicresponses based on randomized replicated treatments conducted in testplots of agronomic environments; aggregate the data representing theagronomic responses into subsets of the data representing the agronomicresponses, each subset of the data representing the agronomic responsesassociated with one of a number of performance zones; receivecharacteristics associated with a portion of a field and determine thatthe portion of the field represents a particular performance zone of thenumber of performance zones based on the characteristics associated withthe portion of the field; recommend a particularized treatment level fora crop located in the portion of the field based on the particularperformance zone; and communicate the particularized treatment level toa machine, the particularized treatment level to be applied to theportion of the field by the machine to optimize an agronomic responsebased on the particular performance zone.
 2. The system of claim 1,wherein the agronomic response comprises a yield of the crop.
 3. Thesystem of claim 1, wherein the characteristics associated with theparticular performance zone comprise at least one of soil environment,landscape, productivity, climate, and weather.
 4. The system of claim 1,the at least one processor further to generate a user interface thatdisplays a graph of predicted yield of the crop based on a treatmentrate for the portion of the field based on the particular performancezone.
 5. The system of claim 1, the at least one processor further togenerate a user interface that displays a map showing the portion of thefield on the map as a user interface element that represents theparticular performance zone.
 6. The system of claim 1, wherein the fieldcomprises a first field and the randomized replicated treatments areassigned to a second field by multiplying a minimum treatment area by apredetermined number of treatment levels and a number of times thepredetermined treatment levels is to be replicated and randomlyassigning a spatial location of each of the treatment levels to bereplicated in a test plot in the second field, carrying out a treatmentfor the spatial location of each of the treatment levels in the testplot in the second field, obtaining data representing an agronomicresponse for the second field, and storing the data representing theagronomic response for the second field in the database.
 7. The systemof claim 1, wherein the machine comprises a treatment dispensing systemto dispense the particularized treatment level of at least one ofseeding, irrigation, nitrogen, fungicide, herbicide, insecticide,pesticide, and growth regulator.
 8. A method comprising: receiving, froma database, by at least one processor, data representing agronomicresponses based on randomized replicated treatments conducted in testplots of agronomic environments; aggregating, by the at least oneprocessor, the data representing the agronomic responses into subsets ofthe data representing the agronomic responses, each subset of the datarepresenting the agronomic responses associated with one of a number ofperformance zones; receiving, by the at least one processor,characteristics associated with a portion of a field and determiningthat the portion of the field represents a particular performance zoneof the number of performance zones based on the characteristicsassociated with the portion of the field; recommending, by the at leastone processor, a particularized treatment level for a crop located inthe portion of the field based on the particular performance zone; andcommunicating, by the at least one processor, the particularizedtreatment level to a machine, the particularized treatment level to beapplied to the portion of the field by the machine to optimize anagronomic response based on the particular performance zone.
 9. Themethod of claim 8, wherein the agronomic response comprises a yield ofthe crop.
 10. The method of claim 8, wherein the characteristicsassociated with the particular performance zone comprise at least one ofsoil environment, landscape, productivity, climate, and weather.
 11. Themethod of claim 8, further comprising generating a user interface thatdisplays a graph of predicted yield of the crop based on a treatmentrate for the portion of the field based on the particular performancezone.
 12. The method of claim 8, further comprising generating a userinterface that displays a map showing the portion of the field on themap as a user interface element that represents the particularperformance zone.
 13. The method of claim 8, wherein the field comprisesa first field and the randomized replicated treatments are assigned to asecond field by multiplying a minimum treatment area by a predeterminednumber of treatment levels and a number of times the predeterminedtreatment levels is to be replicated and randomly assigning a spatiallocation of each of the treatment levels to be replicated in a test plotin the second field, carrying out a treatment for the spatial locationof each of the treatment levels in the test plot in the second field,obtaining data representing an agronomic response for the second field,and storing the data representing the agronomic response for the secondfield in the database.
 14. The method of claim 8, wherein the machinecomprises a treatment dispensing system to dispense the particularizedtreatment level of at least one of seeding, irrigation, nitrogen,fungicide, herbicide, insecticide, pesticide, and growth regulator. 15.A non-transitory computer-readable storage medium, having instructionsstored thereon that, when executed by a computing device cause thecomputing device to perform operations, the operations comprising:receiving, from a database, data representing agronomic responses basedon randomized replicated treatments conducted in test plots of agronomicenvironments; aggregating the data representing the agronomic responsesinto subsets of the data representing the agronomic responses, eachsubset of the data representing the agronomic responses associated withone of a number of performance zones; receiving characteristicsassociated with a portion of a field and determining that the portion ofthe field represents a particular performance zone of the number ofperformance zones based on the characteristics associated with theportion of the field; recommending a particularized treatment level fora crop located in the portion of the field based on the particularperformance zone; and communicating the particularized treatment levelto a machine, the particularized treatment level to be applied to theportion of the field by the machine to optimize an agronomic responsebased on the particular performance zone.
 16. The non-transitorycomputer-readable storage medium of claim 15, wherein the agronomicresponse comprises a yield of the crop.
 17. The non-transitorycomputer-readable storage medium of claim 15, wherein thecharacteristics associated with the particular performance zone compriseat least one of soil environment, landscape, productivity, climate, andweather.
 18. The non-transitory computer-readable storage medium ofclaim 17, the operations further comprising generating a user interfacethat displays a graph of predicted yield of the crop based on atreatment rate for the portion of the field based on the particularperformance zone.
 19. The non-transitory computer-readable storagemedium of claim 17, the operations further comprising generating a userinterface that displays a map showing the portion of the field on themap as a user interface element that represents the particularperformance zone.
 20. The non-transitory computer-readable storagemedium of claim 15, wherein the field comprises a first field and therandomized replicated treatments are assigned to a second field bymultiplying a minimum treatment area by a predetermined number oftreatment levels and a number of times the predetermined treatmentlevels is to be replicated and randomly assigning a spatial location ofeach of the treatment levels to be replicated in a test plot in thesecond field, carrying out a treatment for the spatial location of eachof the treatment levels in the test plot in the second field, obtainingdata representing an agronomic response for the second field, andstoring the data representing the agronomic response for the secondfield in the database.